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Record W2782739831 · doi:10.1115/imece2017-72611

Vibration Analysis of Large Silos: Modeling and Experiments

2017· article· en· W2782739831 on OpenAlexaff
Reza Moazed, Reza Fotouhi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSiloInformation siloStructural engineeringVibrationFinite element methodEngineeringStrain gaugeNatural frequencyAcousticsMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

During the discharge of large cylindrical silos, significant vibrations, pulsations and quaking are often created. The vibrations are typically accompanied by acoustical emissions referred to as silo honking. Such noise pollution is a health and safety concern for workers at site (e.g., hearing loss due to prolonged exposure). From a structural integrity standpoint, the vibrations contribute to the fatigue failure of the load carrying members and connections and endanger the safety and integrity of the overall system. The associated forces and deflections are often further amplified due to resonance between the silo support structure and the discharge loading frequencies. In order to avoid exciting one of the silo support structure modes, researchers and engineers often employ simplified mass-spring models to predict the natural frequencies of the structure. However, these models do not always capture all of the important modes of the silo system. In this paper, a detailed finite element model is developed to predict all of the dominant modes (vertical and side-way swaying) of the system. Beam, shell, and solid elements are used to model the silo support structure, silo walls, and the potash mass, respectively. For the experimental study, several strain gauges and accelerometers were installed on a full-scale silo support structure to determine the frequency of loading during material discharge. This paper summarizes the results of the numerical and experimental study that has been performed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.357
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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